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Clinical Rehabilitation, Vol. 22, No. 2, 143-161 (2008)
DOI: 10.1177/0269215507080130
© 2008 SAGE Publications

A meta-analysis examining clinical test utilities for assessing meniscal injury

Brent B. Meserve

Department of Rehabilitative Medicine, Dartmouth Hitchcock Medical Center, Lebanon, meservebrent{at}adelphia.net

Joshua A. Cleland

Department of Physical Therapy, Franklin Pierce College and Rehabilitation Services of Concord Hospital, Concord

Thomas R. Boucher

Department of Mathematics, Plymouth State University, Plymouth, New Hampshire, USA

Objective: To systematically review the most recent literature with meta-analysis to summarize the accuracy of clinical tests for assessing meniscal lesions of the knee.

Methods and measures: A computerized database search was performed to identify eligible articles. Identified articles were reviewed to determine eligibility and methodological quality. Sensitivity, specificity, likelihood ratios and diagnostic odd ratios were reproduced or recorded from each study. Meta-analysis was performed using the reported study sensitivity and specificity values.

Results: Three tests — joint line tenderness, McMurray's and Apley's — were compared in the meta-analysis. The methodological quality of the studies was found to have a significant effect on both the test sensitivities and specificities. Summary receiver operating characteristic (ROC) curves, sensitivity values, mean likelihood ratios and diagnostic odd ratios (DOR) uniformly show joint line tenderness (DOR = 10.98) to be the best `common' test, followed by McMurray's (DOR = 3.99) and Apley's (DOR = 2.2). Thessaly's test reported the strongest DOR of 227, but samples were smaller (n = 410), than those for joint line tenderness (n = 1354), McMurray's (n = 1232) and Apley's (n = 479).

Conclusion: Methodological quality varied from poor to fair among studies, affecting test performance. Future studies should, where possible, utilize larger samples of individuals without meniscal lesions to better estimate test specificity and thus more accurately identify optimal clinical tests.


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